






'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch




class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__=14   ,	UpperCamelCase__=7   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=False   ,	UpperCamelCase__=True   ,	UpperCamelCase__=99   ,	UpperCamelCase__=32   ,	UpperCamelCase__=4   ,	UpperCamelCase__=4   ,	UpperCamelCase__=4   ,	UpperCamelCase__=37   ,	UpperCamelCase__="gelu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=512   ,	UpperCamelCase__=0.02   ,	):






         """simple docstring"""


         a_						     =			parent
         a_						     =			batch_size
         a_						     =			seq_length
         a_						     =			is_training
         a_						     =			use_input_mask
         a_						     =			use_token_type_ids
         a_						     =			use_labels
         a_						     =			vocab_size
         a_						     =			hidden_size
         a_						     =			rotary_dim
         a_						     =			num_hidden_layers
         a_						     =			num_attention_heads
         a_						     =			intermediate_size
         a_						     =			hidden_act
         a_						     =			hidden_dropout_prob
         a_						     =			attention_probs_dropout_prob
         a_						     =			max_position_embeddings
         a_						     =			initializer_range
         a_						     =			None
         a_						     =			vocab_size - 1
         a_						     =			vocab_size - 1
         a_						     =			vocab_size - 1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size	)

         a_						     =			None
         if self.use_input_mask:
                a_						     =			random_attention_mask([self.batch_size, self.seq_length]	)

         a_						     =			GPTJConfig(
             vocab_size=self.vocab_size   ,	n_embd=self.hidden_size   ,	n_layer=self.num_hidden_layers   ,	n_head=self.num_attention_heads   ,	n_positions=self.max_position_embeddings   ,	use_cache=UpperCamelCase__   ,	bos_token_id=self.bos_token_id   ,	eos_token_id=self.eos_token_id   ,	pad_token_id=self.pad_token_id   ,	rotary_dim=self.rotary_dim   ,	)

         return (config, input_ids, input_mask)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.prepare_config_and_inputs()
         a_     ,	a_     ,	a_						     =			config_and_inputs
         a_						     =			{'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			20
         a_						     =			model_class_name(UpperCamelCase__	)

         a_						     =			model.init_cache(input_ids.shape[0]   ,	UpperCamelCase__	)
         a_						     =			jnp.ones((input_ids.shape[0], max_decoder_length)   ,	dtype='i4'	)

         a_						     =			jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1	)[None, :]   ,	(input_ids.shape[0], input_ids.shape[-1] - 1)	)
         a_						     =			model(
             input_ids[:, :-1]   ,	attention_mask=UpperCamelCase__   ,	past_key_values=UpperCamelCase__   ,	position_ids=UpperCamelCase__   ,	)

         a_						     =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,	dtype='i4'	)
         a_						     =			model(
             input_ids[:, -1:]   ,	attention_mask=UpperCamelCase__   ,	past_key_values=outputs_cache.past_key_values   ,	position_ids=UpperCamelCase__   ,	)

         a_						     =			model(UpperCamelCase__	)

         a_						     =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
         self.parent.assertTrue(diff < 1e-3   ,	msg=f'Max diff is {diff}'	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			20
         a_						     =			model_class_name(UpperCamelCase__	)

         a_						     =			jnp.concatenate(
             [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]   ,	axis=-1   ,	)

         a_						     =			model.init_cache(input_ids.shape[0]   ,	UpperCamelCase__	)
         a_						     =			jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1	)[None, :]   ,	(input_ids.shape[0], input_ids.shape[-1] - 1)	)

         a_						     =			model(
             input_ids[:, :-1]   ,	attention_mask=UpperCamelCase__   ,	past_key_values=UpperCamelCase__   ,	position_ids=UpperCamelCase__   ,	)
         a_						     =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,	dtype='i4'	)
         a_						     =			model(
             input_ids[:, -1:]   ,	past_key_values=outputs_cache.past_key_values   ,	attention_mask=UpperCamelCase__   ,	position_ids=UpperCamelCase__   ,	)

         a_						     =			model(UpperCamelCase__   ,	attention_mask=UpperCamelCase__	)

         a_						     =			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
         self.parent.assertTrue(diff < 1e-3   ,	msg=f'Max diff is {diff}'	)





@require_flax
class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Optional[int]      			=     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  _a  : List[str]      			=     (FlaxGPTJForCausalLM,) if is_flax_available() else ()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			FlaxGPTJModelTester(self	)







  def    _a       (			self	):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
                a_     ,	a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
                a_     ,	a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__	)







  @tooslow
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			GPTaTokenizer.from_pretrained('gpt2'   ,	pad_token='<|endoftext|>'   ,	padding_side='left'	)
         a_						     =			tokenizer(['Hello this is a long string', 'Hey']   ,	return_tensors='np'   ,	padding=UpperCamelCase__   ,	truncation=UpperCamelCase__	)

         a_						     =			FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'	)
         a_						     =			False
         a_						     =			model.config.eos_token_id

         a_						     =			jax.jit(model.generate	)

         a_						     =			jit_generate(
             inputs['input_ids']   ,	attention_mask=inputs['attention_mask']   ,	pad_token_id=tokenizer.pad_token_id	).sequences

         a_						     =			tokenizer.batch_decode(UpperCamelCase__   ,	skip_special_tokens=UpperCamelCase__	)

         a_						     =			[
             'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
             'Hey, I\'m a little late to the party. I\'m going to',
         ]

         self.assertListEqual(UpperCamelCase__   ,	UpperCamelCase__	)







  @is_pt_flax_cross_test
  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__	):
                       # prepare inputs
                       a_						     =			self._prepare_for_class(UpperCamelCase__   ,	UpperCamelCase__	)
                       a_						     =			{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       a_						     =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       a_						     =			getattr(UpperCamelCase__   ,	UpperCamelCase__	)

                       a_     ,	a_						     =			pt_inputs['input_ids'].shape
                       a_						     =			np.random.randint(0   ,	seq_length - 1   ,	size=(batch_size,)	)
                       for batch_idx, start_index in enumerate(UpperCamelCase__	):
                              a_						     =			0
                              a_						     =			1
                              a_						     =			0
                              a_						     =			1
                       a_						     =			pt_model_class(UpperCamelCase__	).eval()
                       a_						     =			model_class(UpperCamelCase__   ,	dtype=jnp.floataa	)

                       a_						     =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,	UpperCamelCase__	)
                       a_						     =			fx_state

                       with torch.no_grad():
                              a_						     =			pt_model(**UpperCamelCase__	).to_tuple()

                       a_						     =			fx_model(**UpperCamelCase__	).to_tuple()
                       self.assertEqual(len(UpperCamelCase__	)   ,	len(UpperCamelCase__	)   ,	'Output lengths differ between Flax and PyTorch'	)
                       for fx_output, pt_output in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                              self.assert_almost_equals(fx_output[:, -1]   ,	pt_output[:, -1].numpy()   ,	4e-2	)

                       with tempfile.TemporaryDirectory() as tmpdirname:
                              pt_model.save_pretrained(UpperCamelCase__	)
                              a_						     =			model_class.from_pretrained(UpperCamelCase__   ,	from_pt=UpperCamelCase__	)

                       a_						     =			fx_model_loaded(**UpperCamelCase__	).to_tuple()
                       self.assertEqual(
                           len(UpperCamelCase__	)   ,	len(UpperCamelCase__	)   ,	'Output lengths differ between Flax and PyTorch'	)
                       for fx_output_loaded, pt_output in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                              self.assert_almost_equals(fx_output_loaded[:, -1]   ,	pt_output[:, -1].numpy()   ,	4e-2	)







  @is_pt_flax_cross_test
  def    _a       (			self	):






         """simple docstring"""


         a_     ,	a_						     =			self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__	):
                       # prepare inputs
                       a_						     =			self._prepare_for_class(UpperCamelCase__   ,	UpperCamelCase__	)
                       a_						     =			{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                       # load corresponding PyTorch class
                       a_						     =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
                       a_						     =			getattr(UpperCamelCase__   ,	UpperCamelCase__	)

                       a_						     =			pt_model_class(UpperCamelCase__	).eval()
                       a_						     =			model_class(UpperCamelCase__   ,	dtype=jnp.floataa	)

                       a_						     =			load_flax_weights_in_pytorch_model(UpperCamelCase__   ,	fx_model.params	)
                       a_     ,	a_						     =			pt_inputs['input_ids'].shape
                       a_						     =			np.random.randint(0   ,	seq_length - 1   ,	size=(batch_size,)	)
                       for batch_idx, start_index in enumerate(UpperCamelCase__	):
                              a_						     =			0
                              a_						     =			1
                              a_						     =			0
                              a_						     =			1

                       # make sure weights are tied in PyTorch
                       pt_model.tie_weights()

                       with torch.no_grad():
                              a_						     =			pt_model(**UpperCamelCase__	).to_tuple()

                       a_						     =			fx_model(**UpperCamelCase__	).to_tuple()
                       self.assertEqual(len(UpperCamelCase__	)   ,	len(UpperCamelCase__	)   ,	'Output lengths differ between Flax and PyTorch'	)
                       for fx_output, pt_output in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                              self.assert_almost_equals(fx_output[:, -1]   ,	pt_output[:, -1].numpy()   ,	4e-2	)

                       with tempfile.TemporaryDirectory() as tmpdirname:
                              fx_model.save_pretrained(UpperCamelCase__	)
                              a_						     =			pt_model_class.from_pretrained(UpperCamelCase__   ,	from_flax=UpperCamelCase__	)

                       with torch.no_grad():
                              a_						     =			pt_model_loaded(**UpperCamelCase__	).to_tuple()

                       self.assertEqual(
                           len(UpperCamelCase__	)   ,	len(UpperCamelCase__	)   ,	'Output lengths differ between Flax and PyTorch'	)
                       for fx_output, pt_output in zip(UpperCamelCase__   ,	UpperCamelCase__	):
                              self.assert_almost_equals(fx_output[:, -1]   ,	pt_output[:, -1].numpy()   ,	4e-2	)







  @tooslow
  def    _a       (			self	):






         """simple docstring"""


         for model_class_name in self.all_model_classes:
                a_						     =			model_class_name.from_pretrained('EleutherAI/gpt-j-6B'	)
                a_						     =			model(np.ones((1, 1)	)	)
                self.assertIsNotNone(UpperCamelCase__	)












'''simple docstring'''







import cmath
import math


def       __UpperCamelCase					(      lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ):
       """simple docstring"""




       a_						     =			math.radians(lowercase_   )
       a_						     =			math.radians(lowercase_   )

       # Convert voltage and current to rectangular form
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )

       # Calculate apparent power
       return voltage_rect * current_rect


if __name__ == "__main__":
  import doctest

  doctest.testmod()




